Contributions to Adaptive Educational Hypermedia Systems Via On-Line Learning Style Estimation

By Botsios, Sotiris; Georgiou, Demetrius et al. | Educational Technology & Society, April 2008 | Go to article overview

Contributions to Adaptive Educational Hypermedia Systems Via On-Line Learning Style Estimation


Botsios, Sotiris, Georgiou, Demetrius, Safouris, Nikolaos, Educational Technology & Society


Introduction

Education via internet represents great and exciting opportunities for both educators and learners. Wagschal (1998) suggests that the internet and World Wide Web have made the computer a dynamic force in distance education, providing a new and interactive means of overcoming time and distance to reach learners. As there is an increasing demand for e-learning procedures tailored to the needs of learners, Learning Management Systems (LMSs) designers take under consideration adaptivity issues. An approach to adaptive learning is considered to be Adaptive Educational Hypermedia Systems (AEHSs). According to Brusilovsky (2001) Adaptive Hypermedia is an alternative to the traditional "one-size-fits-all" approach in the development of Hypermedia Systems. Adaptive Hypermedia Systems build a model of the goals, preferences and knowledge of each individual user, and use this model throughout the interaction with the user, in order to adapt to the needs of that user. For example, a student in an AEHS will be given a presentation that is adapted specifically to his or her knowledge of subject and a suggested set of most relevant links to proceed further. In Papanikolaou, Mabbott et al (2006) work they consider Learning Styles (LSs) among adaptivity parameters and play a crucial role in AEHSs (Papanikolaou, Mabbott et al, 2006).

Learning Theories diverge with respect to the fact that students learn and acquire knowledge in many different ways, which have been classified as LSs. Felder and Silverman (1988) claim that students learn by observing and hearing; reflecting and acting or by reasoning logically and intuitively. Students also learn by memorizing and visualizing; drawing analogies and building mathematical models. Learning behavior has been extensively examined in cognitive psychology. There exists a great variety of models and theories in the literature regarding learning behavior and cognitive characteristics i.e. LSs or Cognitive Styles (CSs) i.e. Sternberg and Zhang (2001). Although some authors do not distinguish between LSs and CSs (Kaltz, Rezaei, 2004), there are others who clearly do (Papanikolaou, Mabbott et al, 2006, Smith, 2001). According to Riding and Rayner (1998) CS refers to an individual's method of processing information. The building up of a repertoire of learning strategies that combine with cognitive style, contribute to an individual's LS. In particular, as Jonassen and Grabowski (1993) reported, LSs are applied CSs, removed one more level from pure processing ability usually referring to learners' preferences on how they process information and not to actual ability, skill or processing tendency. Our research in not intended in focusing on such differences in terminology, so the terms LS and CS will be used interchangably. LSs classifications have been proposed by Kolb (1984) and others (Honey and Mumford, 1992; Dunn, Dunn, 1985; Dunn, Dunn, 1992; Felder, Silverman, 1988; Murray, 1999). Most of the authors categorize LSs and/or CSs into groups and propose certain inventories and methodologies capable of classifying learners accordingly. Despite their efforts for classifications, most of the above authors notice that the LS variable is a continuous. That means that an effort for dichotomous classification of learners could be proved pointless. Most of the proposed LS diagnostic methodologies are addressed to educators.

Educators are humans and are therefore flexible systems. They can adapt to unfamiliar situations in class, and they are able to gather information in an efficient manner, disregarding irrelevant details. The information, which is gathered, could be general, qualitative and vague because humans can reason, infer, and deduce new information. Subsequently, educators exploit all the information concerning their students in order to teach in the best possible way. Educators have common sense, as they can make decisions on teaching strategy, and provide logical explanations for those decisions, as referred by De Koning et al (2000). …

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